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Machine Learning Algorithms Aided Disease Diagnosis and Prediction of Grape Leaf

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Intelligent Systems (ICMIB 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 728))

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Abstract

The range of diseases that can affect grape leaves has made it vital to analyze them. High-end data analytics and predictive analysis are required for a number of diseases, including black rot esca black measles, blight isariopsis, and others, in order to predict disease occurrence. For the prediction of leaf diseases, convolution neural networks combined with data augmentation have increased the degree of verification. For illness predictive analytics, a proper confusion matrix for support vector machines driven by CNN was created. Along with k-mean clustering, fuzzy logic with accurate feature extraction, and color moment definition, we also compared our results with these techniques. The findings indicate a higher effectiveness of up to 95% in correctly predicting grapes leaf disease.

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Authors and Affiliations

  1. Computer Science Engineering Department (AIT CSE (AIML)), Chandigarh University, Gharuan, Punjab, India

    Priyanka Kaushik

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  1. Priyanka Kaushik
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Correspondence to Priyanka Kaushik .

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Editors and Affiliations

  1. School of Computer and Information Sciences, University of Hyderabad, Hyderabad, Telangana, India

    Siba K. Udgata

  2. Department of Computer Science and Engineering, Indira Gandhi Institute of Technology, Dhenkanal, Odisha, India

    Srinivas Sethi

  3. Department of Computer Science, University of Eastern Finland, Kuopio, Finland

    Xiao-Zhi Gao

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Kaushik, P. (2024). Machine Learning Algorithms Aided Disease Diagnosis and Prediction of Grape Leaf. In: Udgata, S.K., Sethi, S., Gao, XZ. (eds) Intelligent Systems. ICMIB 2023. Lecture Notes in Networks and Systems, vol 728. Springer, Singapore. https://doi.org/10.1007/978-981-99-3932-9_2

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  • DOI: https://doi.org/10.1007/978-981-99-3932-9_2

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-3931-2

  • Online ISBN: 978-981-99-3932-9

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